Gastos Casa
18 August, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 26/3/2025 | Comida | 4000 | Andrés | avena multigrano y chucrut |
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 22/4/2025 | Comida | 107881 | Tami | Supermercado |
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 28/4/2025 | Comida | 13050 | Tami | Cervezas MUT |
| 29/4/2025 | Electricidad | 52507 | Andrés | enel |
| 29/4/2025 | Diosi | 11990 | Andrés | arena 7kg superzoo |
| 3/5/2025 | Agua | 17072 | Andrés | aguas andina |
| 13/5/2025 | VTR | 22000 | Andrés | NA |
| 17/5/2025 | Electricidad | 52404 | Andrés | NA |
| 13/6/2025 | VTR | 22000 | Andrés | NA |
| 22/6/2025 | Electricidad | 52401 | Andrés | NA |
| 27/7/2025 | Electricidad | 52000 | Andrés | NA |
| 27/7/2025 | Comida | 59147 | Tami | Supermercado |
| 29/7/2025 | Comida | 10000 | Andrés | complemento comida |
| 29/7/2025 | Electrodomésticos/mantención casa | 68000 | Andrés | NA |
| 30/7/2025 | Comida | 24140 | Tami | Supermercado |
| 31/7/2025 | Gas | 19100 | Andrés | NA |
| 3/8/2025 | Comida | 86089 | Tami | Supermercado |
| 10/8/2025 | Comida | 108649 | Tami | Supermercado |
| 12/8/2025 | Enceres | 13490 | Tami | Confort |
| 16/8/2025 | VTR | 22000 | Andrés | NA |
| 17/8/2025 | Comida | 72586 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0137e+09 2 5.2344 0.0055 **
## lag_depvar 2.6338e+11 1 2719.9269 <2e-16 ***
## Residuals 8.2113e+10 848
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1771.303 16228.98 0.1434059
## 2-0 31326.336 23231.117 39421.55 0.0000000
## 2-1 24097.498 19416.066 28778.93 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 850 68503.71 2 56125.29
## 851 62945.00 2 68503.71
## 852 50209.71 2 62945.00
## 853 49436.29 2 50209.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 696 53560.60 21923.651
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71 35271.00 34774.86 48788.71 50717.71 51727.43 51313.14
## [848] 56125.29 68503.71 62945.00 50209.71 49436.29
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [852] 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2014.804423 4038.622892 -536.300725 2439.606991 -2966.129418
## 7 8 9 10 11
## 520.019046 -5653.976659 -1189.810828 -3968.346206 -422.301515
## 12 13 14 15 16
## -4943.418803 -1615.715770 -906.001177 371.787478 -3247.167504
## 17 18 19 20 21
## -383.516952 -2134.861967 6598.893858 -1528.929172 -1208.781355
## 22 23 24 25 26
## 1474.697756 -1186.029585 234.679961 1695.568195 -7099.983800
## 27 28 29 30 31
## 944.890588 8191.229860 423.438535 -8.618959 -2395.395136
## 32 33 34 35 36
## 1579.555106 4577.035991 1134.588656 2399.463219 -1858.791744
## 37 38 39 40 41
## 4615.194720 4308.043623 -2268.594280 -2976.740855 -1108.063117
## 42 43 44 45 46
## -10740.357832 7282.858842 2556.666303 1368.140149 8107.284660
## 47 48 49 50 51
## 693.698492 6535.887099 6725.231436 -5868.122252 -4787.046185
## 52 53 54 55 56
## -5055.666912 -7928.476553 6124.430067 -4077.021409 -4897.487909
## 57 58 59 60 61
## 3849.665497 885.155350 -33.824553 140.727751 -4997.621696
## 62 63 64 65 66
## 18122.296398 3649.370814 -3635.899661 5931.848832 7353.951414
## 67 68 69 70 71
## 14652.810162 1715.749404 -13191.636027 -1296.643692 4651.082814
## 72 73 74 75 76
## -4890.311631 -4399.031529 -10495.474831 2461.734224 -5402.292569
## 77 78 79 80 81
## 1058.151289 -6870.253622 539.044685 -2360.901416 -2700.775741
## 82 83 84 85 86
## -3939.513976 -546.605000 2306.489074 3757.208970 474.629193
## 87 88 89 90 91
## -486.220620 194.831795 4300.519814 -1161.406666 1151.512453
## 92 93 94 95 96
## -2063.103157 -1044.417901 176.830262 274.281947 -7484.257078
## 97 98 99 100 101
## 2386.808934 -8605.145109 -2948.209904 -4047.719203 -1746.190884
## 102 103 104 105 106
## -1270.382814 3172.539715 -2347.210465 2588.150589 -1161.779573
## 107 108 109 110 111
## 966.868645 2584.656397 -3154.802308 -4725.342051 -855.150140
## 112 113 114 115 116
## 1898.838968 11690.771214 -1237.502304 2672.256196 4267.900665
## 117 118 119 120 121
## 3509.947218 -1091.213177 -4709.263974 -3720.782839 2320.444697
## 122 123 124 125 126
## -1730.431313 1341.402970 8860.098558 854.522533 137.610981
## 127 128 129 130 131
## -2514.788375 2659.249957 7058.008375 1021.879772 -8490.384909
## 132 133 134 135 136
## 1751.745601 4138.955277 -3158.203294 -1416.633879 -852.074213
## 137 138 139 140 141
## -3878.781443 1181.756097 -495.737860 -2914.043074 1716.026674
## 142 143 144 145 146
## -1881.757701 -7831.002882 2033.079306 -3483.913669 2096.397339
## 147 148 149 150 151
## -261.207803 1019.622613 -361.608964 1349.969828 1185.568599
## 152 153 154 155 156
## 3356.425180 -4859.760817 -1175.737809 -3237.607038 5953.151820
## 157 158 159 160 161
## 9747.350931 -3651.946928 -4999.382029 3381.316454 -24.212324
## 162 163 164 165 166
## 2478.156403 -6126.455864 -6966.477481 3934.596117 17173.435622
## 167 168 169 170 171
## 3411.081125 -615.451950 -2663.996652 -1323.591299 3370.136383
## 172 173 174 175 176
## -448.331033 -8296.309011 2640.147202 4102.237306 402.775431
## 177 178 179 180 181
## 8527.172861 -9470.642226 -3696.623228 -10970.642713 -11468.712469
## 182 183 184 185 186
## 1004.020066 9063.101733 -1658.395166 5699.623174 6326.052602
## 187 188 189 190 191
## 12927.313537 8196.761480 -4301.501611 2222.310749 10122.538390
## 192 193 194 195 196
## -1893.774609 -2697.371967 -10535.131221 -6617.826390 979.690421
## 197 198 199 200 201
## -5486.800689 -10048.123276 5134.002581 -3316.898261 -1959.549310
## 202 203 204 205 206
## -1050.113154 6249.052085 9633.908857 324.826279 2669.477020
## 207 208 209 210 211
## 2840.902695 5525.737015 12572.894943 -5952.225288 -11559.012141
## 212 213 214 215 216
## -5924.993775 -10843.029592 -5326.133763 1279.013149 -13257.341000
## 217 218 219 220 221
## 16147.826327 7562.506517 1277.558784 26436.220639 12261.117446
## 222 223 224 225 226
## 7062.749852 13750.456639 -4196.065779 -2021.336773 3498.863472
## 227 228 229 230 231
## 81.878386 2470.486170 8731.171734 5558.083766 -2175.408400
## 232 233 234 235 236
## -2093.326093 9163.353212 -11772.220547 -7542.260205 -8796.769287
## 237 238 239 240 241
## -10353.601533 2829.479171 1103.817899 -8545.054208 -9233.950628
## 242 243 244 245 246
## 8854.097878 -8009.273209 2246.430965 -10543.143341 -4292.067785
## 247 248 249 250 251
## 1185.750238 764.747769 -12555.536341 3408.134097 1824.425458
## 252 253 254 255 256
## 3971.354876 1890.728190 -1407.170490 10891.728209 20625.038766
## 257 258 259 260 261
## 2924.318613 -4548.248350 3834.239756 -1973.041253 3459.825978
## 262 263 264 265 266
## -5131.351412 -11169.531705 -4996.751428 -786.254695 -5452.279223
## 267 268 269 270 271
## 8517.455616 -4548.841410 3923.189201 -2377.602655 4160.942147
## 272 273 274 275 276
## 434.340756 7026.748106 -1696.008141 11741.505894 -4881.359440
## 277 278 279 280 281
## 1431.436182 -668.354893 7555.803150 -5361.243942 -3028.088510
## 282 283 284 285 286
## -11553.268699 -2945.744596 18382.906416 7489.147740 2430.350243
## 287 288 289 290 291
## -935.057755 601.429894 6093.638358 6570.683259 -19091.444510
## 292 293 294 295 296
## -11427.280429 -8389.655468 9411.660531 2806.085497 -1448.517361
## 297 298 299 300 301
## 27135.264781 9756.059369 4577.705478 9190.357565 2517.900907
## 302 303 304 305 306
## -1370.507110 7565.502807 -24633.716820 -3824.179552 -453.602671
## 307 308 309 310 311
## -7242.096578 -4229.565875 2684.556065 -9442.841140 -3461.338768
## 312 313 314 315 316
## -8409.593819 1358.646040 -3361.299208 1842.981597 -4294.007969
## 317 318 319 320 321
## 27239.062870 -1006.696072 3009.716765 10542.590614 5280.993981
## 322 323 324 325 326
## 32064.471713 4737.340909 -21309.066033 1473.533962 794.705847
## 327 328 329 330 331
## -6776.511670 -2026.198962 -33550.885210 708.291357 -2474.446806
## 332 333 334 335 336
## -258.059501 -3331.509003 3929.071016 -604.032377 -7119.434288
## 337 338 339 340 341
## -3268.450246 -2338.224993 -7823.405418 3722.799958 -1514.048385
## 342 343 344 345 346
## -1881.298065 -1137.058884 31.958912 332.548717 -1772.910739
## 347 348 349 350 351
## -9601.253692 -13346.498793 2199.728960 -4446.404143 -3777.206792
## 352 353 354 355 356
## -6096.014129 1642.623144 1263.538313 2620.636787 -3914.132263
## 357 358 359 360 361
## -661.221173 527.050497 6855.255657 93.571786 -225.914808
## 362 363 364 365 366
## 2392.222987 -2950.711370 -1071.103466 -8935.323522 -4792.769025
## 367 368 369 370 371
## -6367.209530 -5088.720263 -7381.242296 4902.085978 235.391010
## 372 373 374 375 376
## 6976.443750 -7806.621153 -2413.763230 -3535.602783 -2608.406677
## 377 378 379 380 381
## -12595.673591 1796.362232 -10753.874303 5600.551621 9213.669905
## 382 383 384 385 386
## 2966.691458 -2573.767298 1432.106667 6561.860772 11202.994941
## 387 388 389 390 391
## -6049.763502 -5596.591673 -377.780250 8341.257148 1564.022660
## 392 393 394 395 396
## 10964.443221 -10171.905487 2511.429872 441.649026 290.493077
## 397 398 399 400 401
## -925.733711 -831.000626 -14751.387147 8312.876296 -1413.378833
## 402 403 404 405 406
## -1597.656605 6763.672387 -8172.027201 -1506.260080 -2733.278149
## 407 408 409 410 411
## -6009.238707 -3026.888326 -4074.522516 -8900.070713 6015.847874
## 412 413 414 415 416
## 1496.381501 -7527.722899 -7825.143702 14109.255412 3645.290945
## 417 418 419 420 421
## 4300.258255 -8248.507726 -4929.823276 -2770.384002 2658.799694
## 422 423 424 425 426
## -14183.739771 -2917.685883 -9219.433151 2918.527538 6864.302526
## 427 428 429 430 431
## 6430.166408 -4161.966472 -4284.473676 -4874.379759 -1928.837224
## 432 433 434 435 436
## -5849.214898 -6749.201179 -6055.614583 -1486.512705 -944.809873
## 437 438 439 440 441
## -5077.711673 2487.562074 4727.406590 -5193.418214 -2283.685078
## 442 443 444 445 446
## 1451.911765 -3973.274385 2706.928964 -6722.228293 -12237.228229
## 447 448 449 450 451
## -4604.043685 9559.300562 -2157.544913 4630.214849 -6014.381223
## 452 453 454 455 456
## -1252.010183 253.617001 2890.341703 -12418.206729 3254.831497
## 457 458 459 460 461
## -6831.912939 6407.975291 2871.710583 2353.103123 -4010.393355
## 462 463 464 465 466
## 1938.452535 -170.667224 1627.881023 -693.592819 3178.602077
## 467 468 469 470 471
## -2822.537062 5630.673395 -7134.468436 -3132.123494 -2362.587300
## 472 473 474 475 476
## -4813.735152 2860.550525 7649.938907 -6190.387063 1330.265923
## 477 478 479 480 481
## -6337.996335 -2984.654767 1878.833768 -13071.538091 -9860.733984
## 482 483 484 485 486
## -1284.880613 -66.311910 -1056.184239 -1439.840808 -9685.312367
## 487 488 489 490 491
## 11015.903849 6115.290498 7277.063544 -5604.752634 5214.497878
## 492 493 494 495 496
## 9122.397945 5855.090418 -13688.494388 -10736.786573 -3579.449355
## 497 498 499 500 501
## -1235.222176 -652.579122 -7754.989011 502.343079 4173.524017
## 502 503 504 505 506
## 5378.443618 511.554349 -71.258816 -7391.776620 437.746583
## 507 508 509 510 511
## -5184.117854 1709.579571 -1427.472893 -8287.442842 -710.908407
## 512 513 514 515 516
## -2785.818696 -695.586709 1221.520967 -9614.006057 -7861.185584
## 517 518 519 520 521
## 24206.239763 9668.571892 5693.067526 -5537.957255 2613.691537
## 522 523 524 525 526
## 16827.468909 11234.232619 -24416.329719 -5249.526645 -3905.960897
## 527 528 529 530 531
## 4411.346804 -530.974669 -11275.225713 4251.730047 13755.534898
## 532 533 534 535 536
## -5172.180111 4194.259502 5363.162806 -1997.105263 -4739.634439
## 537 538 539 540 541
## -7256.731141 -2260.425403 8169.412529 -50.132900 -8317.799902
## 542 543 544 545 546
## 1664.646766 -756.486936 211.059760 -11187.340260 -11186.514113
## 547 548 549 550 551
## 1944.768630 6896.476203 -1449.156651 706.649100 -7855.946941
## 552 553 554 555 556
## 8449.580199 762.954812 -12092.440350 9046.380581 8510.560451
## 557 558 559 560 561
## -75.926887 4677.435854 -3764.330025 13929.798596 21278.961115
## 562 563 564 565 566
## -6746.187576 -9933.515297 6557.092071 -7.395013 3225.844609
## 567 568 569 570 571
## -7610.716915 -17513.152840 6514.258453 6268.602229 1720.003498
## 572 573 574 575 576
## 2913.737421 1579.810496 -2356.527087 14535.384599 -9871.021395
## 577 578 579 580 581
## -6434.677462 8541.640293 2665.068461 -6743.987328 7332.723868
## 582 583 584 585 586
## -3995.401615 -2956.727799 15532.011968 -14713.096891 8259.480527
## 587 588 589 590 591
## -120.595984 -6399.948466 -920.310855 90.229925 -10813.833227
## 592 593 594 595 596
## 1668.008653 -7279.046350 2952.597381 8740.496015 -7653.118327
## 597 598 599 600 601
## 5719.894639 2585.448942 6698.406756 -3366.187816 5984.565450
## 602 603 604 605 606
## -8478.932510 2100.878945 1110.416353 2976.707420 1326.697569
## 607 608 609 610 611
## 227.188817 -5979.204181 7924.675185 -1354.584206 -2738.533123
## 612 613 614 615 616
## -3607.718476 -8370.975165 11839.425140 4784.365251 -9478.060215
## 617 618 619 620 621
## 11490.242913 5883.551987 -5752.105841 26200.222362 -13048.187886
## 622 623 624 625 626
## -6958.555855 3001.567479 -4319.761972 -10738.716669 11178.897843
## 627 628 629 630 631
## -21781.883934 -2506.841659 8586.150534 11022.119417 -1694.295068
## 632 633 634 635 636
## 33147.652090 -6802.801397 5525.874938 5200.407301 -2473.154309
## 637 638 639 640 641
## -5536.425658 -2111.822632 -12593.355012 -2371.612219 -2009.520959
## 642 643 644 645 646
## -2639.027258 -2971.107441 1707.786007 4317.871748 16840.282715
## 647 648 649 650 651
## 18388.221976 677.513360 4587.418756 10405.352399 19927.129075
## 652 653 654 655 656
## 488.613191 -28305.250996 -1505.934520 -2445.339349 1726.032630
## 657 658 659 660 661
## -3332.292476 -10749.748663 1562.546453 4122.017895 -1118.503945
## 662 663 664 665 666
## 12924.067517 1227.597483 1681.269223 -11823.551340 1271.990574
## 667 668 669 670 671
## 1078.952735 -5274.890635 -7507.375732 1984.249487 -3797.966458
## 672 673 674 675 676
## 2593.544577 -3464.490261 -9416.960187 -8373.019169 -3036.344586
## 677 678 679 680 681
## 113.007316 2782.137391 639.896083 -3903.767712 -1882.208251
## 682 683 684 685 686
## -1392.069434 -8317.055593 4583.366924 -2318.376999 -1473.485937
## 687 688 689 690 691
## 511.678227 10774.547338 9753.069394 10513.847640 -9783.766805
## 692 693 694 695 696
## -3655.618450 -3234.109291 5782.093614 -10481.582793 -7991.824980
## 697 698 699 700 701
## -8681.521305 -6335.804603 -4796.444489 3026.434897 -4465.340441
## 702 703 704 705 706
## -1959.736917 4159.497082 31036.474126 9440.266211 23371.145654
## 707 708 709 710 711
## 1618.816044 8268.693622 22874.215109 6528.999991 -18226.470377
## 712 713 714 715 716
## 4798.561106 -5463.727403 -122.738233 456.891778 -17290.178894
## 717 718 719 720 721
## -5295.642745 3301.247429 -3045.454974 -13011.444851 4241.911474
## 722 723 724 725 726
## -5591.681615 705.238184 -3971.082847 -12484.723464 1325.940668
## 727 728 729 730 731
## -1907.173619 -9817.539207 17225.988478 1739.912163 -2757.189341
## 732 733 734 735 736
## 5679.931970 -8663.337956 -759.373041 8102.489232 -15383.857328
## 737 738 739 740 741
## -5949.201296 7368.299835 -4820.865908 122.985829 1790.426896
## 742 743 744 745 746
## -1992.026884 -5204.639171 6374.466184 -6310.143472 22661.419004
## 747 748 749 750 751
## 7799.688268 -1971.461011 -7314.184692 23386.951614 -4309.728379
## 752 753 754 755 756
## 1375.335314 -14443.005662 56080.815215 26925.765922 15116.844599
## 757 758 759 760 761
## -10616.483282 10627.669345 7339.214918 5835.527478 -46362.878766
## 762 763 764 765 766
## -16176.697046 949.965223 -2532.704164 -3474.110322 122826.122840
## 767 768 769 770 771
## 19389.700759 43805.571268 22696.801161 12192.879692 15959.738807
## 772 773 774 775 776
## 25838.997878 -98690.121826 -6733.845512 -35817.410658 1719.801634
## 777 778 779 780 781
## -1243.221682 3381.869272 -7424.949651 -1460.425749 -1960.396653
## 782 783 784 785 786
## 3409.183588 -7166.004436 -2252.341222 3903.737800 2254.762196
## 787 788 789 790 791
## -2766.386216 -4055.944419 1728.221567 2845.686771 -55.545299
## 792 793 794 795 796
## -6706.764468 -5790.775585 -1159.278103 -1274.496198 -7828.242170
## 797 798 799 800 801
## -2370.066692 -3284.341463 -2682.511679 10707.187317 2235.175360
## 802 803 804 805 806
## 7076.406361 2926.702460 -5442.660962 8188.317591 9882.553572
## 807 808 809 810 811
## -10586.634893 -7390.719753 -7505.513202 3000.095227 4192.095776
## 812 813 814 815 816
## -2267.588831 -14164.447884 -4120.841044 6247.723701 8232.171931
## 817 818 819 820 821
## -9670.364752 -7754.467781 -9345.546756 9738.986929 -1228.006670
## 822 823 824 825 826
## -4376.667621 -8454.116632 7862.341799 7909.749377 4978.249187
## 827 828 829 830 831
## -3095.771434 -708.042628 2895.228614 4673.860010 1632.750343
## 832 833 834 835 836
## -6685.180231 2091.848605 -393.880543 2757.148723 4146.113407
## 837 838 839 840 841
## -1128.525399 -9328.471606 5675.796375 -4857.289393 -7577.025432
## 842 843 844 845 846
## 3018.199784 -13044.293830 -2827.818733 11619.253232 1311.836755
## 847 848 849 850 851
## 637.214805 -658.718581 4515.164266 12691.795749 -3675.335220
## 852 853
## -11556.943436 -1210.359981
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17254.48 20100.38 24352.44 24070.54 26422.84 23756.70 24472.69 19706.95
## 10 11 12 13 14 15 16 17
## 19443.63 16787.59 17564.70 14295.57 14346.72 15011.07 16706.88 15027.66
## 18 19 20 21 22 23 24 25
## 16061.86 15435.68 22514.93 21599.35 21079.45 22968.60 22294.89 22947.15
## 26 27 28 29 30 31 32 33
## 24792.27 18723.40 20448.77 28282.56 28340.19 28013.25 25643.73 27045.54
## 34 35 36 37 38 39 40 41
## 30886.84 31235.11 32643.65 30155.38 34134.96 37341.59 34399.03 31211.35
## 42 43 44 45 46 47 48 49
## 30059.64 20643.43 28158.76 30594.15 31682.86 38517.87 38012.68 42672.77
## 50 51 52 53 54 55 56 57
## 46907.12 39608.33 34179.24 29204.19 22351.71 28638.88 25221.06 21520.33
## 58 59 60 61 62 63 64 65
## 25926.70 27185.68 27482.56 27894.19 23766.99 40350.77 42193.90 37442.01
## 66 67 68 69 70 71 72 73
## 41647.05 46560.48 57223.82 55238.49 40488.36 37995.35 41011.88 35314.60
## 74 75 76 77 78 79 80 81
## 30768.90 21476.55 24676.58 20604.13 22689.25 17587.10 19601.62 18828.49
## 82 83 84 85 86 87 88 89
## 17856.66 15926.46 17203.65 20810.08 25225.80 26215.22 26240.17 26856.62
## 90 91 92 93 94 95 96 97
## 30979.84 29810.92 30809.82 28875.13 28075.31 28443.29 28849.69 22430.05
## 98 99 100 101 102 103 104 105
## 25443.72 18477.35 17334.00 15375.62 15675.24 16352.32 20822.92 19906.85
## 106 107 108 109 110 111 112 113
## 23416.35 23206.42 24881.77 27757.23 25256.48 21701.58 21976.88 24621.94
## 114 115 116 117 118 119 120 121
## 35481.50 33675.17 35511.81 38508.77 40463.78 38153.26 32976.64 29319.70
## 122 123 124 125 126 127 128 129
## 31401.57 29682.31 30863.33 38459.62 38102.25 37164.22 34029.18 35809.56
## 130 131 132 133 134 135 136 137
## 41204.98 40645.53 31851.25 33115.47 36303.77 32716.06 31104.07 30189.50
## 138 139 140 141 142 143 144 145
## 26748.10 28161.88 27931.61 25618.97 27642.47 26267.86 19872.92 22902.06
## 146 147 148 149 150 151 152 153
## 20729.75 23705.49 24245.23 25834.89 26016.89 27670.29 28970.43 32001.19
## 154 155 156 157 158 159 160 161
## 27473.45 26736.75 24293.13 30184.51 41672.38 40003.38 37369.54 42387.50
## 162 163 164 165 166 167 168 169
## 43795.42 47209.74 42677.76 37987.12 43409.85 59704.49 61915.59 60330.43
## 170 171 172 173 174 175 176 177
## 57157.59 55557.58 58258.90 57283.45 49579.14 52401.33 56142.22 56178.40
## 178 179 180 181 182 183 184 185
## 63303.93 53810.62 50563.07 41376.00 32919.27 36425.90 46524.68 45980.95
## 186 187 188 189 190 191 192 193
## 51930.95 57673.26 68451.24 73731.64 67429.26 67622.60 74689.63 70368.09
## 194 195 196 197 198 199 200 201
## 65892.99 55141.83 49174.74 50598.37 46195.12 38367.57 44789.33 43017.55
## 202 203 204 205 206 207 208 209
## 42655.68 43133.81 49924.66 58809.75 58439.52 60163.53 61818.55 65607.96
## 210 211 212 213 214 215 216 217
## 75070.08 67156.58 55351.14 49962.46 40962.99 37922.13 41034.34 31059.17
## 218 219 220 221 222 223 224 225
## 48024.78 55342.16 56243.64 78998.45 86489.96 88492.26 96080.07 87035.19
## 226 227 228 229 230 231 232 233
## 81036.42 80618.55 77270.09 76431.97 81166.77 82530.41 76968.47 72183.65
## 234 235 236 237 238 239 240 241
## 77834.65 64488.69 56528.91 48483.32 40098.81 44288.75 46440.48 39894.24
## 242 243 244 245 246 247 248 249
## 33576.76 43854.42 38104.00 42037.86 34305.35 33011.82 36665.40 39487.96
## 250 251 252 253 254 255 256 257
## 30321.72 36257.00 40056.65 45248.99 47966.03 47458.84 57754.96 75243.97
## 258 259 260 261 262 263 264 265
## 75059.11 68372.90 69854.04 66076.60 67522.07 61282.67 50562.32 46591.54
## 266 267 268 269 270 271 272 273
## 46800.85 42909.40 51709.41 47984.24 52129.03 50246.49 54311.94 54607.82
## 274 275 276 277 278 279 280 281
## 60622.44 58257.78 67926.22 61853.85 62063.78 60413.63 66153.82 59887.23
## 282 283 284 285 286 287 288 289
## 56452.70 46009.89 44407.38 61631.57 67159.08 67568.34 64987.14 64074.93
## 290 291 292 293 294 295 296 297
## 68074.03 71982.44 52987.85 43094.51 37108.34 47424.91 50665.23 49779.59
## 298 299 300 301 302 303 304 305
## 73964.65 79907.29 80574.64 85184.96 83384.36 78416.93 81882.15 56792.61
## 306 307 308 309 310 311 312 313
## 53055.46 52735.38 46528.42 43739.16 47340.84 39896.48 38619.17 33183.21
## 314 315 316 317 318 319 320 321
## 36966.01 36147.73 39977.44 37962.79 63737.27 61579.43 63202.27 71196.72
## 322 323 324 325 326 327 328 329
## 73582.96 99052.94 97431.35 73272.61 72071.01 70429.08 62384.48 59508.03
## 330 331 332 333 334 335 336 337
## 29470.14 33156.02 33595.35 35914.22 35255.36 41019.75 42094.86 37344.59
## 338 339 340 341 342 343 344 345
## 36559.37 36685.98 32007.06 38003.33 38666.44 38924.77 39800.18 41585.31
## 346 347 348 349 350 351 352 353
## 43406.48 43158.25 36106.07 26678.13 32020.40 30881.92 30472.16 28089.66
## 354 355 356 357 358 359 360 361
## 32766.46 36519.08 40980.70 39170.51 40430.24 42567.74 49959.71 50510.06
## 362 363 364 365 366 367 368 369
## 50711.63 53173.71 50658.25 50103.04 42751.48 39949.50 36128.15 33907.81
## 370 371 372 373 374 375 376 377
## 29967.34 37252.04 39537.98 47420.05 41394.33 40841.75 39379.69 38912.67
## 378 379 380 381 382 383 384 385
## 29784.35 34380.45 27435.16 35650.90 45979.45 49543.34 47817.46 49808.28
## 386 387 388 389 390 391 392 393
## 56025.72 65507.05 58721.31 53191.92 52920.74 60297.12 60820.27 69485.19
## 394 395 396 397 398 399 400 401
## 58595.57 60161.78 59722.08 59206.16 57693.71 56455.82 43220.12 51802.09
## 402 403 404 405 406 407 408 409
## 50802.94 49769.61 56168.17 48713.83 48025.28 46352.67 42031.75 40862.95
## 410 411 412 413 414 415 416 417
## 38927.64 33024.29 40893.76 43818.87 38493.43 33583.74 48449.14 52292.31
## 418 419 420 421 422 423 424 425
## 56219.94 48692.25 45017.10 43693.63 47278.60 35702.54 35431.86 29693.04
## 426 427 428 429 430 431 432 433
## 35280.55 43604.69 50493.97 47260.76 44330.67 41257.12 41145.36 37624.63
## 434 435 436 437 438 439 440 441
## 33764.61 30999.80 32575.24 34423.85 32429.30 37293.45 43496.42 40250.11
## 442 443 444 445 446 447 448 449
## 39956.23 42961.42 40848.36 44836.23 40085.09 31121.04 29958.99 41311.26
## 450 451 452 453 454 455 456 457
## 40992.93 46641.81 42279.72 42629.24 44249.09 47965.78 37844.17 42691.48
## 458 459 460 461 462 463 464 465
## 38116.60 45682.58 49201.18 51820.68 48551.55 50891.38 51092.83 52839.16
## 466 467 468 469 470 471 472 473
## 52336.97 55279.54 52608.90 57658.04 50920.69 48532.59 47119.31 43745.02
## 474 475 476 477 478 479 480 481
## 47499.63 54959.96 49389.16 51091.71 45882.65 44262.31 47094.11 36512.59
## 482 483 484 485 486 487 488 489
## 30076.74 31945.31 34640.90 36130.27 37095.74 30739.10 43264.28 49921.79
## 490 491 492 493 494 495 496 497
## 56749.32 51462.93 56294.03 63924.62 67734.49 53996.36 44578.02 42603.79
## 498 499 500 501 502 503 504 505
## 42926.86 43717.70 38206.66 40604.62 45903.98 51583.30 52292.69 52403.21
## 506 507 508 509 510 511 512 513
## 46107.68 47447.12 43707.85 46462.19 46128.01 39846.34 40976.96 40152.44
## 514 515 516 517 518 519 520 521
## 41257.62 43896.58 36739.61 32020.90 55900.86 64058.22 67709.67 61091.45
## 522 523 524 525 526 527 528 529
## 62430.39 76010.48 82984.33 57944.81 52816.96 49512.65 53889.83 53396.37
## 530 531 532 533 534 535 536 537
## 43583.98 48573.75 61229.04 55752.17 59148.41 63134.53 60188.35 55221.16
## 538 539 540 541 542 543 544 545
## 48686.14 47342.59 55276.42 55026.94 47590.07 49812.77 49639.51 50333.05
## 546 547 548 549 550 551 552 553
## 40985.94 32825.09 37165.10 45278.30 45075.35 46780.52 40792.85 49802.05
## 554 555 556 557 558 559 560 561
## 50956.87 40740.33 50277.30 58136.78 57501.99 61098.19 56867.20 68622.75
## 562 563 564 565 566 567 568 569
## 85304.33 75399.52 63967.91 68385.25 66510.44 67696.57 59270.15 43266.03
## 570 571 572 573 574 575 576 577
## 50271.68 56174.28 57356.55 59431.19 60077.96 57205.62 69447.02 58824.96
## 578 579 580 581 582 583 584 585
## 52550.65 60148.93 61652.27 54749.28 61013.12 56591.16 53636.99 67201.24
## 586 587 588 589 590 591 592 593
## 52636.09 59977.17 59069.95 52794.88 52100.34 52376.26 43096.13 45891.76
## 594 595 596 597 598 599 600 601
## 40520.55 44764.50 53523.98 46858.11 52714.55 55091.31 60757.90 56917.72
## 602 603 604 605 606 607 608 609
## 61729.36 53301.69 55180.87 55956.86 58264.02 58837.81 58378.78 52558.75
## 610 611 612 613 614 615 616 617
## 59617.30 57678.25 54776.72 51484.26 44450.29 55955.49 59841.20 50780.61
## 618 619 620 621 622 623 624 625
## 61178.02 65361.11 58853.78 81071.47 66200.84 58533.58 60535.62 55891.00
## 626 627 628 629 630 631 632 633
## 46230.67 56933.31 37498.27 37358.56 46922.59 57400.58 55446.06 84162.23
## 634 635 636 637 638 639 640 641
## 74352.84 76552.59 78189.15 72917.85 65640.39 62276.21 50186.61 48555.66
## 642 643 644 645 646 647 648 649
## 47447.74 45930.68 44316.07 46991.70 51607.00 66571.06 80988.77 78113.44
## 650 651 652 653 654 655 656 657
## 79016.79 84885.59 98324.10 93085.11 63368.79 60821.77 57777.54 58761.72
## 658 659 660 661 662 663 664 665
## 55204.32 45621.45 48004.70 52320.50 51513.08 63069.55 62947.30 63236.69
## 666 667 668 669 670 671 672 673
## 51697.44 53056.33 54074.32 49415.23 43397.75 46431.25 44031.17 47516.35
## 674 675 676 677 678 679 680 681
## 45269.82 38110.73 32771.20 32768.71 35516.43 40246.25 42505.62 40511.07
## 682 683 684 685 686 687 688 689
## 40534.64 40983.20 35328.20 41654.66 41152.34 41451.46 43446.02 54148.79
## 690 691 692 693 694 695 696 697
## 62602.15 70647.62 59949.48 55959.11 52842.91 57994.58 48291.97 41993.95
## 698 699 700 701 702 703 704 705
## 35892.52 32613.16 31093.85 36597.91 34862.31 35534.65 41464.81 70110.88
## 706 707 708 709 710 711 712 713
## 76266.57 93805.47 90126.45 92720.50 107738.57 106579.76 83952.30 84299.44
## 714 715 716 717 718 719 720 721
## 75641.88 72745.97 70723.46 53461.36 48861.90 52352.31 49858.30 38978.66
## 722 723 724 725 726 727 728 729
## 44543.97 40817.05 43061.08 40937.29 31649.06 35597.89 36222.82 29861.44
## 730 731 732 733 734 735 736 737
## 47920.37 50166.90 48201.78 53852.91 46263.23 46537.65 54515.14 40973.34
## 738 739 740 741 742 743 744 745
## 37387.13 45884.15 42660.30 44162.14 46929.46 46043.07 42463.96 49449.29
## 746 747 748 749 750 751 752 753
## 44472.87 65424.60 70742.18 66853.47 58792.91 78561.87 71639.66 70559.43
## 754 755 756 757 758 759 760 761
## 55804.18 104499.38 121561.16 126147.77 107683.19 110110.21 109358.04 107388.31
## 762 763 764 765 766 767 768 769
## 60090.55 45149.32 47057.56 45682.82 43660.45 152175.58 156610.14 181801.34
## 770 771 772 773 774 775 776 777
## 185365.98 179306.83 177305.29 184183.84 81455.42 72049.55 38441.91 41873.08
## 778 779 780 781 782 783 784 785
## 42281.85 46677.24 41079.00 41398.83 41241.53 45792.72 40532.77 40230.41
## 786 787 788 789 790 791 792 793
## 45341.67 48364.81 46620.23 43970.92 46708.17 50073.97 50479.62 45026.20
## 794 795 796 797 798 799 800 801
## 41064.28 41648.92 42058.81 36694.21 36775.91 36048.94 35939.67 47535.68
## 802 803 804 805 806 807 808 809
## 50263.45 56872.44 59019.80 53586.97 60745.30 68475.06 57351.43 50429.23
## 810 811 812 813 814 815 816 817
## 44284.76 48092.76 52458.59 50630.31 38645.98 36951.42 44525.26 52871.22
## 818 819 820 821 822 823 824 825
## 44526.75 38913.55 32623.01 43794.29 43972.67 41379.12 35554.23 44715.11
## 826 827 828 829 830 831 832 833
## 52755.47 57216.34 54061.47 53391.63 55953.00 59742.54 60396.04 53703.72
## 834 835 836 837 838 839 840 841
## 55524.02 54942.99 57187.03 60359.24 58523.47 49760.63 55210.43 50771.88
## 842 843 844 845 846 847 848 849
## 44521.51 48315.29 37602.68 37169.46 49405.88 51090.21 51971.86 51610.12
## 850 851 852 853
## 55811.92 66620.34 61766.66 50646.65
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.809
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.234392 0.7675628 3.952951
## t2* 2719.926944 164.5010960 890.853877
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.215869 5.170306 13.6318
## 2 lag_depvar 1658.528105 2764.195557 4524.0949Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Aug 18 01:02:44 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 18 01:02:54 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 18 01:03:03 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 18 01:03:13 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 18 01:03:23 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 18 01:03:33 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 18 01:03:43 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 18 01:03:53 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Aug 18 01:04:03 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 18 01:04:13 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 8.205857 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 192.101143 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 54.522571 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 1.884286 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 26.407143 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 14.927000 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 15.711429 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 313.759429 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2808, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2808 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-09-09 00:04:58 sería de: 26.848 pesos// Percentil 95% más alto proyectado: 35.188,75
Según TimeGPT: La proyección de la UF a 298 días más 2026-07-04 sería de: 40.201,26 pesos// Percentil 80% más alto proyectado: 40.564,89 pesos// Percentil 95% más alto proyectado: 41.589,24
Según prophet: La proyección de la UF a 298 días más 2026-07-04 sería de: 42.517 pesos// Percentil 95% más alto proyectado: 50.181
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26460.95 | 26321.73 |
| Lo.80 | 26594.36 | 26486.94 |
| Point.Forecast | 26848.21 | 26799.01 |
| Hi.80 | 31608.29 | 32186.25 |
| Hi.95 | 34460.82 | 35038.08 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4788 1028.6175
## s.e. 0.1010 41.4439
##
## sigma^2 = 38077: log likelihood = -521.14
## AIC=1048.28 AICc=1048.61 BIC=1055.35paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4750 849.7566 5.4671
## s.e. 0.1014 348.7572 10.5782
##
## sigma^2 = 38456: log likelihood = -521.01
## AIC=1050.02 AICc=1050.57 BIC=1059.44forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 601.3384 | 592.9478 | 566.8658 |
| Lo.80 | 752.5218 | 743.7341 | 659.1232 |
| Point.Forecast | 1038.1140 | 1028.5762 | 876.3411 |
| Hi.80 | 1323.7061 | 1313.4183 | 1165.1444 |
| Hi.95 | 1474.8895 | 1464.2046 | 1354.7715 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.1.0
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.8.13
## [10] tidytext_0.4.3 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.11.0
## [16] xts_0.14.1 forecast_8.24.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.1.0
## [28] tidyr_1.3.1 tibble_3.3.0 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.9.0 lattice_0.22-6
## [34] GGally_2.3.0 ggplot2_3.5.2 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.9.1 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [43] stringi_1.8.7 DataExplorer_0.8.4 data.table_1.17.8
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 httr2_1.2.1
## [4] lifecycle_1.0.4 StanHeaders_2.32.10 doParallel_1.0.17
## [7] globals_0.18.0 vroom_1.6.5 MASS_7.3-60.2
## [10] crosstalk_1.2.1 magrittr_2.0.3 sass_0.4.10
## [13] rmarkdown_2.29 jquerylib_0.1.4 yaml_2.3.10
## [16] fracdiff_1.5-3 doRNG_1.8.6.2 askpass_1.2.1
## [19] pkgbuild_1.4.8 DBI_1.2.3 abind_1.4-8
## [22] quadprog_1.5-8 nnet_7.3-19 rappdirs_0.3.3
## [25] sandwich_3.1-1 inline_0.3.21 data.tree_1.1.0
## [28] tokenizers_0.3.0 listenv_0.9.1 anytime_0.3.12
## [31] spatial_7.3-17 parallelly_1.45.1 codetools_0.2-20
## [34] xml2_1.3.8 tidyselect_1.2.1 farver_2.1.2
## [37] urca_1.3-4 its.analysis_1.6.0 matrixStats_1.5.0
## [40] stats4_4.4.0 jsonlite_2.0.0 ellipsis_0.3.2
## [43] Formula_1.2-5 iterators_1.0.14 systemfonts_1.2.3
## [46] foreach_1.5.2 tools_4.4.0 glue_1.8.0
## [49] xfun_0.52 TTR_0.24.4 ggfortify_0.4.19
## [52] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [55] fastmap_1.2.0 boot_1.3-30 openssl_2.3.3
## [58] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [61] R6_2.6.1 lfe_3.1.1 colorspace_2.1-1
## [64] networkD3_0.4.1 gtools_3.9.5 generics_0.1.4
## [67] htmlwidgets_1.6.4 ggstats_0.10.0 pkgconfig_2.0.3
## [70] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [73] S7_0.2.0 selectr_0.4-2 janeaustenr_1.0.0
## [76] htmltools_0.5.8.1 carData_3.0-5 tseries_0.10-58
## [79] snakecase_0.11.1 knitr_1.50 rstudioapi_0.17.1
## [82] tzdb_0.5.0 uuid_1.2-1 nlme_3.1-164
## [85] curl_6.4.0 cachem_1.1.0 KernSmooth_2.23-22
## [88] parallel_4.4.0 fBasics_4041.97 pillar_1.11.0
## [91] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [94] car_3.1-3 dbplyr_2.5.0 xtable_1.8-4
## [97] evaluate_1.0.4 mvtnorm_1.3-3 cli_3.6.5
## [100] compiler_4.4.0 crayon_1.5.3 rngtools_1.5.2
## [103] future.apply_1.20.0 labeling_0.4.3 rstan_2.32.7
## [106] QuickJSR_1.8.0 viridisLite_0.4.2 assertthat_0.2.1
## [109] lazyeval_0.2.2 Matrix_1.7-0 hms_1.1.3
## [112] bit64_4.6.0-1 future_1.67.0 nixtlar_0.6.2
## [115] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [118] bslib_0.9.0 quantmod_0.4.28 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))